Examen 3¶
Para el desarrollo del tercer examen, usar los datos del archivo “Ex_1” para las preguntas del 01 al 03, mientras que para las preguntas 04 al 06 usar el archivo usar el archivo “Ex_2”, mientras que para las preguntas 07 y 08 usar el archivo “Ex_3”. Finalmente, para las preguntas 09 y 10 se va modificar el script del ejercicio de la Sesión 04 del Módulo 06, simular el modelo modificando el umbral de los coeficiente rugosidad, (umbral mínimo 0.06 y umbral máximo 0.08) para las llanuras izquierda y derecha.
In [1]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import rasterio
import geopandas as gpd
In [2]:
# 1. Obtener una gráfica que muestre el raster con una leyenda que indique el rango de los valores.
img1 = rasterio.open('./Data Examen 3/Ex_1/Rst/landsatImage.tif')
from rasterio import plot
# Leer la primera banda
band1 = img1.read(1) # Leer la primera banda del raster
# Crear la figura
fig, ax = plt.subplots(figsize=(8,6))
# Mostrar el raster con color 'viridis' y barra de color
img_plot = ax.imshow(band1, cmap="viridis") # Mostrar la imagen
plt.colorbar(img_plot, ax=ax, label="Valores del Raster") # Agregar barra de color
# Agregar título
plt.title("Imagen Landsat")
# Mostrar el gráfico
plt.show()
In [ ]:
# 2. Obtener gráficas usando Canny filter con 02 valores de sigma.
from skimage import feature
redBand, greenBand, blueBand = img1.read(1), img1.read(2), img1.read(3)
redBand = np.float32(redBand)
greenBand = np.float32(greenBand)
blueBand = np.float32(blueBand)
# Aplicar filtro de Canny con dos valores de sigma
edges_02_sigma = feature.canny(redBand, sigma=2) # Sigma bajo
# Crear una figura con subplots
fig, axes = plt.subplots(1, 2, figsize=(20, 10))
# Mostrar imagen original
axes[0].imshow(redBand, cmap=plt.cm.gray)
axes[0].set_title('Imagen Original (Banda Roja)')
axes[0].axis('off')
# Mostrar imagen con Canny (sigma bajo)
axes[1].imshow(edges_02_sigma, cmap=plt.cm.gray)
axes[1].set_title('Canny Filter (Sigma = 2)')
axes[1].axis('off')
plt.show()
In [ ]:
print("Red Band Stats:", np.min(redBand), np.max(redBand), np.mean(redBand))
redFalse = np.zeros(redBand.shape)
redFalse[redBand < 30] = 1
fig, ax = plt.subplots(figsize=(10, 6))
im = ax.imshow(redFalse, cmap='coolwarm') # Mapa de colores para visualización
plt.colorbar(im, shrink=0.5)
plt.title("Hatari Index (Posible Clasificación)")
plt.axis('off')
plt.show()
Red Band Stats: 8.0 166.0 99.69406
In [ ]:
# Aplicar filtro de Canny con dos valores de sigma
edges_02_sigma = feature.canny(redFalse, sigma=2) # Sigma bajo
# Crear una figura con subplots
fig, axes = plt.subplots(1, 2, figsize=(20, 10))
# Mostrar imagen original
axes[0].imshow(redFalse, cmap=plt.cm.gray)
axes[0].set_title('Imagen Original (Banda Roja)')
axes[0].axis('off')
# Mostrar imagen con Canny (sigma bajo)
axes[1].imshow(edges_02_sigma, cmap=plt.cm.gray)
axes[1].set_title('Canny Filter (Sigma = 2)')
axes[1].axis('off')
plt.show()
In [ ]:
# 3. Obtener una gráfica que muestra la delimitación del cuerpo de agua.
from skimage.transform import probabilistic_hough_line
from matplotlib import cm
lines = probabilistic_hough_line(edges_02_sigma, threshold=2, line_length=2, line_gap=1)
# Generating figure 2
fig, axes = plt.subplots(3, 1, figsize=(18, 12), sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(redBand, cmap=cm.gray)
ax[0].set_title('Input image')
ax[1].imshow(edges_02_sigma, cmap=cm.gray)
ax[1].set_title('Canny edges')
ax[2].imshow(edges_02_sigma * 0)
for line in lines:
p0, p1 = line
ax[2].plot((p0[0], p1[0]), (p0[1], p1[1]))
ax[2].set_xlim((0, redBand.shape[1]))
ax[2].set_ylim((redBand.shape[0], 0))
ax[2].set_title('Probabilistic Hough')
for a in ax:
a.set_axis_off()
plt.tight_layout()
plt.show()
In [ ]:
# 4. Obtener una gráfica de la imagen total (OlivoTotal)..
from PIL import Image
from sklearn.cluster import Birch
# Se carga la imagen con PIL, al no poder usar rasterio por el formato
image_path = './Data Examen 3/Ex_2/OlivoTotal.png'
image = Image.open(image_path)
# Convertir la imagen a un array de numpy
image_array = np.array(image)
# Se grafica la imagen
plt.figure(figsize=(6, 6))
plt.imshow(image_array)
plt.title("Imagen OlivoTotal")
plt.axis("off")
plt.show()
In [ ]:
# 5. Representación mediante una gráfica de los puntos cluster luego de Birch.
# Se convierte la imagen a escala de grises para extraer puntos más fácilmente
gray_image = image.convert("L")
gray_array = np.array(gray_image)
# Se describe los valores medio, maximos y minimos de la imagen
print("Gray Image Stats:", np.min(gray_array), np.max(gray_array), np.mean(gray_array))
plt.figure(figsize=(6, 6))
plt.imshow(gray_array, cmap="gray")
plt.title("Imagen OlivoTotal en Escala de Grises")
Gray Image Stats: 1 249 120.77359741912265
Out[ ]:
Text(0.5, 1.0, 'Imagen OlivoTotal en Escala de Grises')
In [ ]:
# Se obtienen las coordenadas de los puntos que cumplen con el umbral, 3/4 de la media de la imagen
threshold = np.mean(gray_array) * 0.75
y_coords, x_coords = np.where(gray_array < threshold)
points = np.column_stack((x_coords, y_coords)) # Coordenadas X, Y
# Aplicar clustering Birch
birch = Birch(branching_factor=10000, n_clusters=None, threshold=2e-5, compute_labels=True)
birch.fit(points)
birchPoint = birch.subcluster_centers_
In [ ]:
# plot clustered points
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.scatter(birchPoint[:,[0]],birchPoint[:,[1]], marker='o',color='orangered',s=1)
plt.imshow(image_array)
plt.title("Imagen OlivoTotal")
plt.show()
In [ ]:
from collections import Counter
threshold = 110
y_coords, x_coords = np.where(gray_array < threshold)
points = np.column_stack((x_coords, y_coords))
# Aumentamos el threshold de Birch para agrupar más puntos por cluster
birch = Birch(branching_factor=10000, n_clusters=None, threshold=4.0, compute_labels=True)
birch.fit(points)
labels = birch.labels_
label_counts = Counter(labels)
# Vamos a filtrar clusters con al menos 100 puntos
min_points = 35
filtered_indices = [i for i, lbl in enumerate(labels) if label_counts[lbl] >= min_points]
filtered_points = points[filtered_indices]
filtered_labels = [labels[i] for i in filtered_indices]
plt.figure(figsize=(10, 10))
plt.imshow(image_array)
plt.scatter(filtered_points[:, 0], filtered_points[:, 1], c=filtered_labels, cmap='rainbow', s=3)
plt.title("Clusters grandes (≥100 puntos)")
plt.axis("off")
plt.show()
In [ ]:
from sklearn.cluster import DBSCAN
from collections import Counter
from scipy.spatial import ConvexHull
# Paso 1: Umbral para binarizar la imagen
threshold = 110
y_coords, x_coords = np.where(gray_array < threshold)
points = np.column_stack((x_coords, y_coords))
# Paso 2: Birch para agrupaciones iniciales
birch = Birch(branching_factor=10000, n_clusters=None, threshold=4.0, compute_labels=True)
birch.fit(points)
labels = birch.labels_
label_counts = Counter(labels)
# Paso 3: Filtro por número mínimo de puntos
min_points = 35
filtered_indices = [i for i, lbl in enumerate(labels) if label_counts[lbl] >= min_points]
filtered_points = points[filtered_indices]
filtered_labels = [labels[i] for i in filtered_indices]
# Paso 4: DBSCAN para limpiar aún más
dbscan = DBSCAN(eps=3, min_samples=5) # Puedes ajustar estos valores
dbscan_labels = dbscan.fit_predict(filtered_points)
# Separar los outliers (label == -1) de los puntos válidos
core_mask = dbscan_labels != -1
core_points = filtered_points[core_mask]
core_labels = dbscan_labels[core_mask]
outliers = filtered_points[~core_mask]
# Paso 5: Visualización
plt.figure(figsize=(10, 10))
plt.imshow(image_array)
plt.scatter(core_points[:, 0], core_points[:, 1], c=core_labels, cmap='rainbow', s=3, label="Puntos válidos")
plt.scatter(outliers[:, 0], outliers[:, 1], color="black", s=3, label="Outliers")
plt.title
Out[ ]:
<function matplotlib.pyplot.title(label: 'str', fontdict: 'dict[str, Any] | None' = None, loc: "Literal['left', 'center', 'right'] | None" = None, pad: 'float | None' = None, *, y: 'float | None' = None, **kwargs) -> 'Text'>
In [ ]:
# 6. Indicar el número de puntos interpretados.
core_labels = np.array(core_labels)
# Eliminamos el label -1 (outliers) y contamos los únicos
n_arboles = len(set(core_labels)) - (1 if -1 in core_labels else 0)
print("Número total de árboles detectados:", n_arboles)
Número total de árboles detectados: 708
In [ ]:
# 7. Representar mediante una gráfica la evolución de suelo a escala de cuenca durante 100 mil años usando los mismo parámetros de los componentes fluviales, hillslope y uplift del ejercicio desarrollado en la Sesion 02 de Módulo 06.
# Añadir imports que no se han puesto
import sys
from rasterio.plot import show
from landlab import HexModelGrid, RasterModelGrid, imshow_grid
from landlab.components import FlowAccumulator, FastscapeEroder, DepressionFinderAndRouter, LinearDiffuser, StreamPowerEroder
# Abrir el raster
img1 = rasterio.open('./Data Examen 3/Ex_3/DEM_18S_clip.tif')
# Leer la primera banda del raster (matriz de elevaciones)
elevArray = img1.read(1)
# Crear figura y ejes
fig, ax = plt.subplots(figsize=(10, 8))
# Mostrar el raster con un colormap
img_plot = ax.imshow(elevArray, cmap='viridis')
plt.colorbar(img_plot, ax=ax, label="Elevación (m)")
# Título
plt.title("Modelo Digital de Elevación - DEM")
# Mostrar el gráfico
plt.show()
show(img1)
plt.show()
In [ ]:
uplift_rate = 0.001 # [m/yr], initially set at 0.001
K_sp = 1.0e-5 # units vary depending on m_sp and n_sp, initially set at 1e-5
m_sp = 0.5 # exponent on drainage area in stream power equation, initially 0.5
n_sp = 1.0 # exponent on slope in stream power equation, initially 1.
K_hs = 0.05 # [m^2/yr], initially 0.05
In [ ]:
# Generación de grilla a partir de atributos de raster
nrows = img1.height
ncols = img1.width
dxy = img1.transform.a
mg = RasterModelGrid(shape=(nrows, ncols), xy_spacing=dxy)
xy_lower_left = (img1.bounds.left, img1.bounds.bottom)
# show number of row, col and resolution
print(nrows, ncols, dxy)
dt = 1000 # time step [yr]
total_time = 0 # amount of time the landscape has evolved [yr]
tmax = 100000 # time for the model loop to run [yr]
t = np.arange(0, tmax, dt)
zr = mg.add_zeros("topographic__elevation", at="node") # add elevation to nodes
zr += elevArray[::-1,:].ravel() # add elevation to nodes
imshow_grid(mg, "topographic__elevation") # show elevation
167 179 90.3109901477272
In [ ]:
# Elevaciones maximas y mínimas en estado inicial
print("Elevación mínima:", np.min(zr))
print("Elevación máxima:", np.max(zr))
Elevación mínima: 2202.0 Elevación máxima: 3403.0
In [ ]:
frr = FlowAccumulator(mg) # create flow routing object
spr = StreamPowerEroder(mg, K_sp=K_sp, m_sp=m_sp, n_sp=n_sp, threshold_sp=0.0) # create stream power eroder object
dfn = LinearDiffuser(mg, linear_diffusivity=K_hs, deposit=False) # create linear diffusive transport object
df = DepressionFinderAndRouter(mg) # create depression finder object
# Simulaión evolución del suelo a escala de cuenca
for ti in t:
zr[mg.core_nodes] += uplift_rate * dt # add uplift to core nodes
dfn.run_one_step(dt) # diffuse the landscape
frr.run_one_step() # route flow
spr.run_one_step(dt) # fluvial incision
total_time += dt # increment time
print("\r" + str(total_time) + " years", end="") # print time
100000 years
In [ ]:
# Visualización de la evolución del suelo
fig, ax = plt.subplots(figsize=(18, 12))
imshow_grid(mg, "topographic__elevation", grid_units=("m", "m"), var_name="Elevation (m)", shrink=0.5 )
title_text = f"$K_{{sp}}$={K_sp}; $K_{{hs}}$={K_hs}; $time$={total_time}; $dx$={dxy}"
plt.title(title_text)
plt.show()
In [ ]:
# 8. Indicar la máxima y mínima elevación del resultado.
max_elev = np.max(zr)
min_elev = np.min(zr)
print("Elevación mínima:", min_elev)
print("Elevación máxima:", max_elev)
print("Cambio de elevación máxima", elevArray.max(), "->", max_elev)
print("Cambio de elevación mínima:", elevArray.min(), "->", min_elev)
print("Diferencia de elevación anterior:", elevArray.max() - elevArray.min())
print("Diferencia de elevación posterior:", max_elev - min_elev)
Elevación mínima: 2202.0 Elevación máxima: 3446.201854400445 Cambio de elevación máxima 3403 -> 3446.201854400445 Cambio de elevación mínima: 2202 -> 2202.0 Diferencia de elevación anterior: 1201 Diferencia de elevación posterior: 1244.201854400445
In [ ]:
print("Elevación mínima:", np.min(band1))
print("Elevación máxima:", np.max(band1))
Elevación mínima: 8 Elevación máxima: 166
In [3]:
import win32com.client
RC = win32com.client.Dispatch("RAS507.HECRASController")
RC.ShowRas()
In [5]:
#Open project
RC.Project_Open(r"C:/Users/juan.guerrero/Juan/dev/apuntes/Examenes/Data Examen 3/Ex_4/Model/test02/s7.prj")
In [6]:
river=RC.Geometry_GetRivers()[1][0]
reach=RC.Geometry_GetReaches(1)[2][0]
nodes=RC.Geometry_GetNodes(1,1)[3]
nodes=list(nodes)
In [7]:
Mann_chan=round(np.random.uniform(0.06,0.08),3)
Mann_bank=round(np.random.uniform(0.06,0.08),3)
print("Mann_chan: ",Mann_chan)
print("Mann_bank: ",Mann_bank)
Mann_chan: 0.073 Mann_bank: 0.071
In [8]:
print(dir(RC))
['AddRef', 'ComputeStartedFromController', 'Compute_Cancel', 'Compute_Complete', 'Compute_CurrentPlan', 'Compute_HideComputationWindow', 'Compute_ShowComputationWindow', 'Compute_WATPlan', 'Create_WATPlanName', 'CurrentGeomFile', 'CurrentGeomHDFFile', 'CurrentPlanFile', 'CurrentProjectFile', 'CurrentProjectTitle', 'CurrentSteadyFile', 'CurrentUnSteadyFile', 'Edit_AddBC', 'Edit_AddIW', 'Edit_AddLW', 'Edit_AddXS', 'Edit_BC', 'Edit_GeometricData', 'Edit_IW', 'Edit_LW', 'Edit_MultipleRun', 'Edit_PlanData', 'Edit_QuasiUnsteadyFlowData', 'Edit_SedimentData', 'Edit_SteadyFlowData', 'Edit_UnsteadyFlowData', 'Edit_WaterQualityData', 'Edit_XS', 'ExportGIS', 'Geometery_GISImport', 'Geometry', 'Geometry_BreachParamGetXML', 'Geometry_BreachParamSetXML', 'Geometry_Get2DFlowAreas', 'Geometry_GetGML', 'Geometry_GetGateNames', 'Geometry_GetMann', 'Geometry_GetNode', 'Geometry_GetNodes', 'Geometry_GetReaches', 'Geometry_GetRivers', 'Geometry_GetStorageAreas', 'Geometry_RatioMann', 'Geometry_SetMann', 'Geometry_SetMann_LChR', 'Geometry_SetSAArea', 'GetDataLocations_Output', 'GetIDsOfNames', 'GetTypeInfo', 'GetTypeInfoCount', 'HECRASVersion', 'Invoke', 'OutputDSS_GetStageFlow', 'OutputDSS_GetStageFlowSA', 'Output_ComputationLevel_Export', 'Output_GetNode', 'Output_GetNodes', 'Output_GetProfiles', 'Output_GetReach', 'Output_GetReaches', 'Output_GetRiver', 'Output_GetRivers', 'Output_Initialize', 'Output_NodeOutput', 'Output_ReachOutput', 'Output_Variables', 'Output_VelDist', 'PlanOutput_IsCurrent', 'PlanOutput_SetCurrent', 'PlanOutput_SetMultiple', 'Plan_GetFilename', 'Plan_GetParameterUncertaintyXML', 'Plan_InformationXML', 'Plan_Names', 'Plan_Reports', 'Plan_SetCurrent', 'Plan_SetParameterUncertaintyXML', 'PlotHydraulicTables', 'PlotPF', 'PlotPFGeneral', 'PlotRatingCurve', 'PlotStageFlow', 'PlotStageFlow_SA', 'PlotXS', 'PlotXYZ', 'Project_Close', 'Project_Current', 'Project_New', 'Project_Open', 'Project_Save', 'Project_SaveAs', 'ProjectionSRSFilename', 'QueryInterface', 'QuitRas', 'Release', 'Schematic_D2FlowAreaPolygon', 'Schematic_ReachCount', 'Schematic_ReachPointCount', 'Schematic_ReachPoints', 'Schematic_StorageAreaPolygon', 'Schematic_XSCount', 'Schematic_XSPointCount', 'Schematic_XSPoints', 'ShowRas', 'ShowRasMapper', 'SteadyFlow_ClearFlowData', 'SteadyFlow_FixedWSBoundary', 'SteadyFlow_SetFlow', 'SteadyFlow_nProfile', 'TablePF', 'TableXS', 'UnsteadyFlow_SetGateOpening_Constant', '_ApplyTypes_', '_FlagAsMethod', '_LazyAddAttr_', '_NewEnum', '_Release_', '_UpdateWithITypeInfo_', '__AttrToID__', '__LazyMap__', '__bool__', '__call__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattr__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__int__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_builtMethods_', '_dir_ole_', '_enum_', '_find_dispatch_type_', '_get_good_object_', '_get_good_single_object_', '_lazydata_', '_make_method_', '_mapCachedItems_', '_oleobj_', '_olerepr_', '_print_details_', '_proc_', '_unicode_to_string_', '_username_', '_wrap_dispatch_', 'wcf_ComputePlan', 'wcf_CreateNewPlan', 'wcf_InputDataLocations_Get', 'wcf_InputDataLocations_Set', 'wcf_OutputDataLocations', 'wcf_SetOutputPlans']
In [9]:
for i in range(19):
Var_Mann=RC.Geometry_SetMann_LChR(river,reach,nodes[i],Mann_bank,Mann_chan,Mann_bank)
print(Var_Mann)
(True, 'River 1 ', 'Reach 1 ', '3909 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '3665 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '3419 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '3203 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '2988 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '2753 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '2528 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '2330 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '2059 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '1857 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '1701 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '1534 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '1336 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '1155 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '922 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '709 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '513 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '339 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '') (True, 'River 1 ', 'Reach 1 ', '100 ', 0.07100000232458115, 0.0729999989271164, 0.07100000232458115, '')
In [10]:
RC.Compute_CurrentPlan(None,None,True)
# RC.Project_Save()
# RC.QuitRas()
Out[10]:
(True,
3,
('Starting Steady Computations', 'Computing', 'Computations Completed'),
True)
In [11]:
water_list,flow_list,veloc_list =[],[],[]
In [12]:
for i in range(19):
water=RC.Output_NodeOutput(1,1,i+1,None,1,2)[0]
flow=RC.Output_NodeOutput(1,1,i+1,None,1,9)[0]
veloc=RC.Output_NodeOutput(1,1,i+1,None,1,23)[0]
water_list.append(water)
flow_list.append(flow)
veloc_list.append(veloc)
In [13]:
water_list
Out[13]:
[2447.566162109375, 2447.54541015625, 2447.5234375, 2447.515380859375, 2447.489501953125, 2447.463134765625, 2447.453857421875, 2446.689208984375, 2444.949951171875, 2444.8564453125, 2443.955810546875, 2442.645751953125, 2442.46240234375, 2442.124755859375, 2441.714599609375, 2441.538330078125, 2441.431396484375, 2441.309326171875, 2441.06640625]
In [14]:
#Display results of water elevation, flow and velocity
output={'Cross Sections':nodes,'Water Surface Elevation(m)':water_list,'Flow(m³/s)':flow_list,'Velocities(m/s)':veloc_list}
df=pd.DataFrame(output)
df.set_index('Cross Sections')
print(len(nodes), len(water_list), len(flow_list), len(veloc_list))
19 19 19 19
In [15]:
df
Out[15]:
| Cross Sections | Water Surface Elevation(m) | Flow(m³/s) | Velocities(m/s) | |
|---|---|---|---|---|
| 0 | 3909 | 2447.566162 | 49.999992 | 0.170982 |
| 1 | 3665 | 2447.545410 | 49.999992 | 0.342401 |
| 2 | 3419 | 2447.523438 | 49.999992 | 0.229937 |
| 3 | 3203 | 2447.515381 | 49.999992 | 0.155260 |
| 4 | 2988 | 2447.489502 | 49.999992 | 0.474583 |
| 5 | 2753 | 2447.463135 | 49.999992 | 0.240510 |
| 6 | 2528 | 2447.453857 | 49.999992 | 0.158589 |
| 7 | 2330 | 2446.689209 | 49.999992 | 3.640512 |
| 8 | 2059 | 2444.949951 | 49.999992 | 0.347901 |
| 9 | 1857 | 2444.856445 | 49.999992 | 0.485823 |
| 10 | 1701 | 2443.955811 | 49.999992 | 3.213779 |
| 11 | 1534 | 2442.645752 | 49.999992 | 0.455180 |
| 12 | 1336 | 2442.462402 | 49.999992 | 0.546407 |
| 13 | 1155 | 2442.124756 | 49.999992 | 0.748684 |
| 14 | 922 | 2441.714600 | 49.999992 | 0.595248 |
| 15 | 709 | 2441.538330 | 49.999992 | 0.405551 |
| 16 | 513 | 2441.431396 | 49.999992 | 0.345754 |
| 17 | 339 | 2441.309326 | 49.999992 | 0.463065 |
| 18 | 100 | 2441.066406 | 49.999992 | 0.526253 |
In [16]:
# 9. Obtener la gráfica de la superficie del agua.
plt.figure(figsize=(12,5))
sns.lineplot(water_list, linestyle="--", color="blue", label="Superficie del agua (m)")
plt.title("Gráfica de la superficie del agua")
plt.xlabel("Distancia progresiva (m)")
plt.ylabel("Elevación del agua (m)")
plt.xlim(0, 19)
plt.grid(True)
plt.legend(loc="best")
plt.show()
In [17]:
# 10. Obtener la gráfica de las velocidades.
plt.figure(figsize=(12,5))
sns.lineplot(veloc_list, linestyle="--", color="green", label="Velocidad (m/s)")
plt.title("Gráfica de las velocidades")
plt.xlabel("Distancia progresiva (m)")
plt.ylabel("Velocidad (m/s)")
plt.grid(True)
plt.xlim(0, 18)
plt.legend(loc="best")
plt.show()
